Automating Behavioral Coding via Text-Mining and Speech Signal Processing

描述（由申请人提供）：大量临床试验表明，动机性访谈（MI; Miller & Rollnick, 2002）是治疗酒精使用障碍（AUD）和相关健康行为问题的有效方法（例如，Burke, Dunn, Atkins, & Phelps, 2005），但对MI的治疗机制知之甚少（Huebner & Tonigan, 2007）。过程研究通常依赖于行为编码方案，如动机性访谈技能代码（MISC; Miller, Moyers, Ernst, & Amrhein, 2008）。尽管MISC的MI机制研究已经产生了一些迄今为止最好的数据（例如，Moyers等人，2007），但行为编码有许多局限性：1)它是明显的劳动密集型，2)编码的客观性，可靠性和可移植性可能具有挑战性，3)它是不灵活的（即，任何新的代码都需要全新的编码）。目前的建议汇集了一个高度跨学科的团队来开发语言处理工具，以自动编码MISC和动机性访谈治疗完整性（MITI; Moyers, Martin, Manuel, Miller, & Ernst, 2007）。这两个系统的编码都基于两种类型的语言数据：说什么和怎么说。我们在计算机科学、认知科学和电子工程方面的团队成员是文本挖掘和语音信号处理方面的领先研究人员，他们的方法将应用于MI转录本和记录，以自动编码MISC/MITI。核心的方法论工具将是主题模型（Steyvers & Griffiths, 2007），语义知识表示的贝叶斯模型。主题模型识别构成意义单元（或主题）的单词分组，最近的扩展模型编码数据（例如，MISC），其中模型学习与特定标签相关的特定文本。目前的提案包含两个具体目标：1)评估主题模型的准确性，使用MI会话的转录本和音频文件自动编码MISC/MITI； 2)使用Aim 1中编码的大约1167个MI会话来测试MI理论（在会话和长期结果中）。这些目标将通过三项心梗干预研究来实现：两项研究侧重于大学生饮酒，一项以医院为基础的药物滥用研究。长期目标是使用创新的语言工具来研究治疗机制，并开发更有效的系统来收集心理治疗过程数据。在死亡、健康并发症、人际关系破裂和经济成本方面，酒精使用障碍继续构成令人难以置信的社会负担。目前的研究将为研究为什么治疗有效提供创新的工具，这反过来可以帮助改善AUD的一些有害影响。

DESCRIPTION (provided by applicant): Numerous clinical trials have shown that Motivational Interviewing (MI; Miller & Rollnick, 2002) is an efficacious treatment for alcohol use disorders (AUD) and related health behavior problems (e.g., Burke, Dunn, Atkins, & Phelps, 2005), but much less is known about the therapy mechanisms of MI (Huebner & Tonigan, 2007). Process research has typically relied on behavioral coding schemes such as the Motivational Interviewing Skills Code (MISC; Miller, Moyers, Ernst, & Amrhein, 2008). Although MI mechanism research with the MISC has produced some of the best data to date (e.g., Moyers et al., 2007), behavioral coding has a number of limitations: 1) it is phenomenally labor intensive, 2) objectivity, reliability, and transportability of coding can be challenging, and 3) it is inflexible (i.e., any new codes require completely new coding). The current proposal brings together a highly interdisciplinary team to develop linguistic processing tools to automate the coding of the MISC and Motivational Interviewing Treatment Integrity (MITI; Moyers, Martin, Manuel, Miller, & Ernst, 2007). The coding of both systems is based on two types of linguistic data: what is said, and how it is said. Our team members in computer science, cognitive science, and electrical engineering are leading researchers in text-mining and speech signal processing, and their methods will be applied to MI transcripts and recordings to automate coding of the MISC/MITI. The core, methodological tool will be topic models (Steyvers & Griffiths, 2007), Bayesian models of semantic knowledge representation. Topic models identify groupings of words that constitute meaning units (or topics), and a recent extension models coded data (e.g., MISC) in which the model learns what specific text is associated with specific tags. Two specific aims encompass the current proposal: 1) Assess the accuracy of topic models to automatically code the MISC/MITI using transcripts and audiofiles of MI sessions, and 2) Test MI theory (within session and long-term outcome) using approximately 1,167 sessions of MI coded in Aim 1. These aims will be accomplished using three MI intervention studies: two studies focused on college student drinking and one hospital-based study of drug abuse. The long-term objectives are to use innovative linguistic tools to study therapy mechanisms and develop more efficient systems for collecting psychotherapy process data. Alcohol use disorders continue to represent an incredible societal burden in terms of death, health complications, fractured relationships, and economic costs. The current research will provide innovative tools for studying why therapy works, which in turn can help to ameliorate some of the deleterious effects of AUD. PUBLIC HEALTH RELEVANCE: Research focused on psychotherapy mechanisms of alcohol use disorders (AUD) have often relied upon behavioral observation coding schemes, such as the Motivational Interview Skills Code (MISC), which are time consuming and can present difficulties with reliability. The current, interdisciplinary proposal develops methods for automating behavioral coding through applying recent advances in text-mining and speech signal processing.